A Diversity-Promoting Objective Function for Neural Conversation Models

著者: Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, Bill Dolan - Conference of the North American Chapter of the Association for Computational Linguistics, NAACL 2016 被引用: 2,025

Sequence-to-sequence neural network models for generation of conversational responses tend to generate safe, commonplace responses (e.g., "I don't know") regardless of the input. We suggest that the traditional objective function, i.e., the likelihood of output (response) given input (message) is unsuited to response generation tasks. Instead we propose using Maximum Mutual Information (MMI) as the objective function in neural models. Experimental results demonstrate that the proposed MMI models produce more diverse, interesting, and appropriate responses, yielding substantive gains in BLEU scores on two conversational datasets and in human evaluations.